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<main>
<article id="content">
<header>
<h1 class="title">Module <code>silk.optimizers.multiple</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from typing import List

import torch
from torch.optim import Optimizer


class MultiOptimizer(Optimizer):
    &#34;&#34;&#34;Simple optimizer container acting as one single optimizer.&#34;&#34;&#34;

    def __init__(self, *optimizers: List[Optimizer]):
        self._optimizers = optimizers

    def __getstate__(self):
        return {&#34;_optimizers&#34;: self._optimizers}

    def __setstate__(self, state):
        self.__dict__.update(state)

    def __repr__(self):
        return f&#34;{self.__class__.__name__}(*{repr(self._optimizers)})&#34;

    def zero_grad(self):
        for op in self._optimizers:
            op.zero_grad()

    def step(self, closure=None):
        loss = None
        if closure is not None:
            with torch.enable_grad():
                loss = closure()

        for op in self._optimizers:
            op.step(closure=None)

        return loss

    @property
    def state(self):
        return self.state_dict()

    @property
    def optimizers(self):
        return self._optimizers

    def state_dict(self):
        return {&#34;_optimizers&#34;: [op.state_dict() for op in self._optimizers]}

    def load_state_dict(self, state_dict):
        for op, s in zip(self._optimizers, state_dict[&#34;_optimizers&#34;]):
            op.load_state_dict(s)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="silk.optimizers.multiple.MultiOptimizer"><code class="flex name class">
<span>class <span class="ident">MultiOptimizer</span></span>
<span>(</span><span>*optimizers: List[torch.optim.optimizer.Optimizer])</span>
</code></dt>
<dd>
<div class="desc"><p>Simple optimizer container acting as one single optimizer.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class MultiOptimizer(Optimizer):
    &#34;&#34;&#34;Simple optimizer container acting as one single optimizer.&#34;&#34;&#34;

    def __init__(self, *optimizers: List[Optimizer]):
        self._optimizers = optimizers

    def __getstate__(self):
        return {&#34;_optimizers&#34;: self._optimizers}

    def __setstate__(self, state):
        self.__dict__.update(state)

    def __repr__(self):
        return f&#34;{self.__class__.__name__}(*{repr(self._optimizers)})&#34;

    def zero_grad(self):
        for op in self._optimizers:
            op.zero_grad()

    def step(self, closure=None):
        loss = None
        if closure is not None:
            with torch.enable_grad():
                loss = closure()

        for op in self._optimizers:
            op.step(closure=None)

        return loss

    @property
    def state(self):
        return self.state_dict()

    @property
    def optimizers(self):
        return self._optimizers

    def state_dict(self):
        return {&#34;_optimizers&#34;: [op.state_dict() for op in self._optimizers]}

    def load_state_dict(self, state_dict):
        for op, s in zip(self._optimizers, state_dict[&#34;_optimizers&#34;]):
            op.load_state_dict(s)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.optim.optimizer.Optimizer</li>
</ul>
<h3>Instance variables</h3>
<dl>
<dt id="silk.optimizers.multiple.MultiOptimizer.optimizers"><code class="name">var <span class="ident">optimizers</span></code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@property
def optimizers(self):
    return self._optimizers</code></pre>
</details>
</dd>
<dt id="silk.optimizers.multiple.MultiOptimizer.state"><code class="name">var <span class="ident">state</span></code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@property
def state(self):
    return self.state_dict()</code></pre>
</details>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.optimizers.multiple.MultiOptimizer.load_state_dict"><code class="name flex">
<span>def <span class="ident">load_state_dict</span></span>(<span>self, state_dict)</span>
</code></dt>
<dd>
<div class="desc"><p>Loads the optimizer state.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>state_dict</code></strong> :&ensp;<code>dict</code></dt>
<dd>optimizer state. Should be an object returned
from a call to :meth:<code>state_dict</code>.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def load_state_dict(self, state_dict):
    for op, s in zip(self._optimizers, state_dict[&#34;_optimizers&#34;]):
        op.load_state_dict(s)</code></pre>
</details>
</dd>
<dt id="silk.optimizers.multiple.MultiOptimizer.state_dict"><code class="name flex">
<span>def <span class="ident">state_dict</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><p>Returns the state of the optimizer as a :class:<code>dict</code>.</p>
<p>It contains two entries:</p>
<ul>
<li>state - a dict holding current optimization state. Its content
differs between optimizer classes.</li>
<li>param_groups - a list containing all parameter groups where each
parameter group is a dict</li>
</ul></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def state_dict(self):
    return {&#34;_optimizers&#34;: [op.state_dict() for op in self._optimizers]}</code></pre>
</details>
</dd>
<dt id="silk.optimizers.multiple.MultiOptimizer.step"><code class="name flex">
<span>def <span class="ident">step</span></span>(<span>self, closure=None)</span>
</code></dt>
<dd>
<div class="desc"><p>Performs a single optimization step (parameter update).</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>closure</code></strong> :&ensp;<code>callable</code></dt>
<dd>A closure that reevaluates the model and
returns the loss. Optional for most optimizers.</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Unless otherwise specified, this function should not modify the
<code>.grad</code> field of the parameters.</p>
</div></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def step(self, closure=None):
    loss = None
    if closure is not None:
        with torch.enable_grad():
            loss = closure()

    for op in self._optimizers:
        op.step(closure=None)

    return loss</code></pre>
</details>
</dd>
<dt id="silk.optimizers.multiple.MultiOptimizer.zero_grad"><code class="name flex">
<span>def <span class="ident">zero_grad</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><p>Sets the gradients of all optimized :class:<code>torch.Tensor</code> s to zero.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>set_to_none</code></strong> :&ensp;<code>bool</code></dt>
<dd>instead of setting to zero, set the grads to None.
This will in general have lower memory footprint, and can modestly improve performance.
However, it changes certain behaviors. For example:
1. When the user tries to access a gradient and perform manual ops on it,
a None attribute or a Tensor full of 0s will behave differently.
2. If the user requests <code>zero_grad(set_to_none=True)</code> followed by a backward pass, <code>.grad</code>\ s
are guaranteed to be None for params that did not receive a gradient.
3. <code>torch.optim</code> optimizers have a different behavior if the gradient is 0 or None
(in one case it does the step with a gradient of 0 and in the other it skips
the step altogether).</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def zero_grad(self):
    for op in self._optimizers:
        op.zero_grad()</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.optimizers" href="index.html">silk.optimizers</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="silk.optimizers.multiple.MultiOptimizer" href="#silk.optimizers.multiple.MultiOptimizer">MultiOptimizer</a></code></h4>
<ul class="two-column">
<li><code><a title="silk.optimizers.multiple.MultiOptimizer.load_state_dict" href="#silk.optimizers.multiple.MultiOptimizer.load_state_dict">load_state_dict</a></code></li>
<li><code><a title="silk.optimizers.multiple.MultiOptimizer.optimizers" href="#silk.optimizers.multiple.MultiOptimizer.optimizers">optimizers</a></code></li>
<li><code><a title="silk.optimizers.multiple.MultiOptimizer.state" href="#silk.optimizers.multiple.MultiOptimizer.state">state</a></code></li>
<li><code><a title="silk.optimizers.multiple.MultiOptimizer.state_dict" href="#silk.optimizers.multiple.MultiOptimizer.state_dict">state_dict</a></code></li>
<li><code><a title="silk.optimizers.multiple.MultiOptimizer.step" href="#silk.optimizers.multiple.MultiOptimizer.step">step</a></code></li>
<li><code><a title="silk.optimizers.multiple.MultiOptimizer.zero_grad" href="#silk.optimizers.multiple.MultiOptimizer.zero_grad">zero_grad</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
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